Continuous-Claude-v3 is a Claude Code development environment that preserves working context between sessions, coordinates specialized agents, and stores project knowledge through ledgers, handoffs, and analysis tools. It is for people using Claude Code on ongoing or complex software work. Its catalogue entries are the skills, agents, hooks, plugin, and setting that provide its workflows and orchestration.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3npx agentmods add agents/parcadei/continuous-claude-v3/memory-extractorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/parcadei/continuous-claude-v3/memory-extractor)<a href="https://agentmods.dev/agents/parcadei/continuous-claude-v3/memory-extractor"><img src="https://agentmods.dev/badge/agents/parcadei/continuous-claude-v3/memory-extractor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/parcadei/continuous-claude-v3/memory-extractor"><img src="https://agentmods.dev/badge/agents/parcadei/continuous-claude-v3/memory-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.01422 |
| Opus 5.5 | $0.00006 | $0.00569 |
| Sonnet 5.5 | $0.00003 | $0.00284 |
| Haiku 4.5 | $0.00002 | $0.00142 |
Grade A, and why
memory-extractor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- memory-extractor — 100% identical, 350 lines differ
How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Extractor Agent
You extract perception changes from Claude Code session transcripts - the "aha moments" where understanding shifts.
Philosophy
"A point of view is worth 80 IQ points" - Alan Kay
We're looking for mental model shifts, not just error→fix pairs:
- Realizations: "Oh, X was actually Y"
- Corrections: "I was wrong about..."
- Insights: "The pattern here is..."
- Surprises: "Unexpected that..."
Input
You receive:
JSONL_PATH: Path to session JSONL fileSESSION_ID: Session identifier (optional, extracted from path if not provided)
Process
Step 1: Extract Thinking Blocks with Perception Signals
# Use the extraction script with filtering
(cd $CLAUDE_PROJECT_DIR/opc && uv run python scripts/core/extract_thinking_blocks.py \
--jsonl "$JSONL_PATH" \
--filter \
--format json) > /tmp/perception-blocks.json
This extracts only thinking blocks containing perception signals (actually, realized, the issue, etc.).
Step 2: Check Stats
(cd $CLAUDE_PROJECT_DIR/opc && uv run python scripts/core/extract_thinking_blocks.py \
--jsonl "$JSONL_PATH" \
--stats)
If 0 blocks with perception signals, skip to Step 5 (output summary with 0 learnings).
Step 3: Classify Perception Changes
Read the extracted blocks from /tmp/perception-blocks.json and classify each one:
| Internal Type | Maps To | Signal | Example |
|---|---|---|---|
REALIZATION |
CODEBASE_PATTERN |
Understanding clicks | "Now I see that X works by..." |
CORRECTION |
ERROR_FIX |
Was wrong, now right | "I was wrong about --depth flag" |
INSIGHT |
CODEBASE_PATTERN |
Pattern discovered | "The issue is schema mismatch" |
DEBUGGING_APPROACH |
WORKING_SOLUTION |
Meta-learning about how to debug | "Test underlying command before wrapper" |
Valid store_learning.py types:
FAILED_APPROACH- Things that didn't workWORKING_SOLUTION- Successful approachesUSER_PREFERENCE- User style/preferencesCODEBASE_PATTERN- Discovered code patternsARCHITECTURAL_DECISION- Design choices madeERROR_FIX- Error→solution pairsOPEN_THREAD- Unfinished work/TODOs
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 176 lines · 16 tokens per session scan A 996e6a1429d9
memory-extractor is an agent published in the GitHub repository parcadei/Continuous-Claude-v3 (3,946 stars, last pushed 8mo ago), licensed MIT. It adds 16 tokens to every session and 1,422 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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